v1.0.0 - Auto-tagging: video in, kohya-ready dataset out

The last mile is connected

vid2dataset now produces complete, trainer-ready datasets. Tick Auto-tag images, type your trigger word, extract — every image gets a .txt caption sidecar (trigger, character tags, general tags) that kohya / OneTrainer read directly.

• WD tagger integration: wd-eva02-large-tagger-v3 (default, most accurate, ~1.2 GB) or wd-swinv2-tagger-v3 (~450 MB). Downloads once on first enable (huggingface.co, hf-mirror.com fallback for CN users).
• Any GPU works: inference runs on onnxruntime-DirectML (~25 MB, on-demand) — NVIDIA, AMD, and Intel GPUs are all accelerated on Windows, with automatic CPU fallback. The .exe itself stays 151 MB.
• kohya folder convention: set kohya_repeats = 10 (+ Flatten output) to get a 10_yourtrigger/ dataset folder.
• vid2dataset tag FOLDER: caption any existing image folder from the CLI — not just vid2dataset output.
• Spot dataset bias instantly: _report.html gains a top-30 tag frequency table; hover any image in _gallery.html to see its tags.
• Caption hygiene built in: single-line LF sidecars, kaomoji-safe underscore handling (^_^ stays ^_^), rating tags never enter captions, cancelled runs never write fake trigger-only captions.

勾选自动打标并填触发词，每张图自动生成可直接训练的 .txt 标签文件。首次启用下载一次模型（约 0.5–1.2 GB），任何显卡（NVIDIA/AMD/Intel）都可通过 DirectML 加速。

Verified end-to-end on real hardware: model + runtime downloads through the actual code paths, DirectML inference on RTX 3090, stdlib-only isolation test mirroring the .exe environment, 100 unit tests.

Scope note: vid2dataset prepares datasets — extraction, curation, captioning. Training, upscaling, image editing, and prompt tools are permanently out of scope.